What is an embedding? Vector representations for similarity and retrieval
Understand embedding vectors through differently worded support questions, including model matching, similarity, retrieval, and limits of truth and access.
Understand the concept
Embeddings map content into numerical vectors for learned similarity comparisons, often supporting retrieval or clustering. Similarity is not factual equivalence or correctness. Use compatible models and metrics rather than mixing equal-dimensional vectors from different models. Keep source text separately; embeddings are not losslessly recoverable document archives.
Semantic similarity can bridge different wording
“How do I request a return?” and “This does not fit; I want to send it back” may be related despite different wording. Retrieval can find material, but returns and exchanges can be similar while requiring different procedures.
OpenAI and Google provide embeddings with different input and task settings; some distinguish query and document roles. Follow model requirements. This page includes no API run or measured Chinese-task ranking.
Model versions and metrics determine compatibility
Equal dimensions do not imply aligned vector spaces. Updating a model may require rebuilding document embeddings for matching queries. Unchanged field names do not establish compatibility; retain versions for comparison during migration.
Scores depend on metrics and normalization and are not directly comparable across systems. Larger values do not establish better retrieval. Check whether evidence is found, including approximate-search and filtering settings.
Text, permissions, and truth require separate handling
After retrieval, read the relevant text with document IDs and positions. Vectors do not establish freshness or authorization. Enforce access at indexing and querying to avoid returning relevant but unauthorized content.
Evaluate paraphrases and confusable questions for retrieval and false matches. This is a validation design without measured gains. Chunking and source quality also matter; not every failure belongs to the embedding model.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.